SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10226–10250 of 15113 papers

TitleStatusHype
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Dynamic Sampling that Adapts: Iterative DPO for Self-Aware Mathematical Reasoning—0
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning—0
Dynamic Temporal Reconciliation by Reinforcement learning—0
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Dyna Planning using a Feature Based Generative Model—0
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees—0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
EasyRL: A Simple and Extensible Reinforcement Learning Framework—0
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning—0
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach—0
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints—0
Ecological Reinforcement Learning—0
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning—0
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning—0
e-COP : Episodic Constrained Optimization of Policies—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified